class_docstr: Cropping layer for 1D input (e.g. temporal sequence).
  
  It crops along the time dimension (axis 1).
  
  Examples:
  
  >>> input_shape = (2, 3, 2)
  >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
  >>> x
  [[[ 0  1]
    [ 2  3]
    [ 4  5]]
   [[ 6  7]
    [ 8  9]
    [10 11]]]
  >>> y = keras.layers.Cropping1D(cropping=1)(x)
  >>> y
  [[[2 3]]
   [[8 9]]]
  
  Args:
      cropping: Int, or tuple of int (length 2), or dictionary.
          - If int: how many units should be trimmed off at the beginning and
            end of the cropping dimension (axis 1).
          - If tuple of 2 ints: how many units should be trimmed off at the
            beginning and end of the cropping dimension
            (`(left_crop, right_crop)`).
  
  Input shape:
      3D tensor with shape `(batch_size, axis_to_crop, features)`
  
  Output shape:
      3D tensor with shape `(batch_size, cropped_axis, features)`
code: def get_config(self):
      config = {"cropping": self.cropping}
      base_config = super().get_config()
      return {**base_config, **config}
  
file_path: keras/keras/layers/reshaping/cropping1d.py
imports: from keras.api_export import keras_export
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
  from keras.utils import argument_validation
label_desc: Use image node_id 4 for calling the Cropping1D obj's underlying member method code with example usage: obj.get_config() and returns: dict